Publications by Patrick Ford
Excess Deaths by Country & Age
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1 ## ...
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All Cardiovascular Death Rate & Diabetes Prevalence
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tidyr) # Load the data data <- read.csv("covid.csv") # Convert date column to Date format...
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Excess Mortality and Vaccination Rate Over Time
install.packages("zoo") ## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3' ## (as 'lib' is unspecified) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequa...
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Cardiovascular Death Rate and Diabetes Prevalence
# Load necessary libraries library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tidyr) # Load the data data <- read.csv("covid.csv") # Convert...
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Cardiovascular Death Rate & Diabetes Prevalence1: 2020-2024
# Load necessary libraries library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tidyr) # Load the data data <- read.csv("covid.csv") # Convert...
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Covid4
4 Plots from the data covid.csv for the period 2020-2024. # Load necessary libraries library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyverse) ## ── At...
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COVID double plot
# Load necessary libraries library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(zoo) ## ## Attaching package: 'zoo' ## The following objects ar...
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Covid
# Load necessary libraries library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyverse) ## ── Attaching core tidyverse packages ────────�...
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Multi Plot Global CO₂ Emission Predictions: 2030 -2039
# Load the required packages pacman::p_load(pacman, tidyverse, ggplot2, gridExtra) # Load the data year <- c(1750, 1800, 1850, 1900, 1950, 2000, 2022, 2023) emissions <- c(0.03, 0.07, 0.28, 1.71, 6.73, 20.64, 37.15, 40.9) # Create a data frame data <- data.frame(year, emissions) # Fit a linear regression model with limited data model <- lm(emis...
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Global CO₂ Emissions: Linear Regression Model Prediction 2030-2039
Given the limitations and uncertainties associated with any predictive model, it’s advisable to treat the predictions as one possible scenario rather than a definitive outcome. However, I would hope for the predictions to be closer to reality than not. library(ggplot2) # Load data from CSV file data <- read.csv("GlobalCO2Emissions.csv") # Ensur...
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